Augmentative or autonomous? Reframing artificial intelligence in talent management through a systematic review

Purpose This paper aims to examine the integration of artificial intelligence (AI) into talent management (TM), focusing on how different AI applications are reconfiguring talent processes and outcomes. Design/methodology/approach Following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a systematic literature review was conducted across four major databases (Scopus, Web of Science, EBSCO Business Source and ProQuest ABI/Inform), identifying 238 records published between 2000 and 2025. After screening and full-text review, 124 peer-reviewed articles were included in the final synthesis. Findings The review reveals two dominant modes of AI adoption in TM: augmentative systems that enhance human judgment and autonomous systems that replace human decision-making. Across recruitment, development, retention and performance management, AI is reshaping processes but remains unevenly theorized. Persistent gaps include limited attention to ethics, fairness and cross-cultural variation, as well as weak integration across micro- and macro-level perspectives. Research limitations/implications The review is limited to English-language, peer-reviewed publications. Future research should examine longitudinal and non-Western contexts and develop integrative theories that link individual-, organizational- and societal-level perspectives on AI in TM. Practical implications The augmentative–autonomous framework provides human resource (HR) and organizational leaders with a lens for evaluating AI adoption choices, balancing efficiency with transparency, fairness and trust. Social implications AI is changing how organizations recruit, develop and manage people, raising important questions of fairness, accountability and trust. This study shows that augmentative applications, which support human decision-making, tend to preserve transparency and employee agency, while autonomous applications, which replace human judgment, increase risks of bias, exclusion and reduced voice. By clarifying these differences, the framework helps policymakers, practitioners and researchers anticipate the societal consequences of AI adoption in TM and design strategies that promote inclusion, equity and responsible use of technology. Originality/value To the best of the authors’ knowledge, this study offers the first comprehensive, PRISMA-compliant systematic review of AI in TM. It introduces a framework that clarifies how augmentative and autonomous AI reshape talent systems, offering a foundation for advancing both scholarship and practice.